Most business organizations today rely on computing power for their business services, including data analysis, supply chain management, inventory tracking, online transactions and customer support. This computing power comes in the form of web services, web portals and other open source or proprietary applications hosted in either leased or owned data centers. These data centers have become a significant user of electrical power both through the data center computational appliances and indirectly though the humidity and thermal conditioners. Recent data show that almost 50% of power delivered to a server farm is spent on cooling infrastructure, while less than 50% is actually utilized in server consumption. The amount of electrical power used during the computational activity inside the server translates into the thermal load. The amount of electrical power spent to maintain the operational temperature is also dependent on the server air flow characteristics and the relative location of the server hardware within the rack and many other parameters as described later in this disclosure. Even though there is a direct relationship between the computational power utilized by the data center(s) and supplied electrical power, the factors affecting that relationship are many, and the instrumentation and analysis needed to quantify them to the required precision for effective control is challenging. Existing power control mechanisms do not attempt to correlate such utilization with given electrical supply units and hence fall short of global optimization of the power utilization in data centers and server installations. This disclosure describes the systematic procedure and apparatus to achieve such monitoring and control using collaborative server computational power measurements and electrical power units consumed under different environmental operational conditions. This method provides the necessary adaptive learning required to address diverse data center server farms and its infrastructure installations. The heuristics used in this approach take into account the server hardware thermal and electrical requirements and their locations inside the server rack and relative locations within the data center zones.